The seminal works of [1]} and [2]} show that word embeddings such as word2vec [3]} and GloVe [4]} contain stereotypical biases using diagnostic methods like word analogies and association tests.
For example, [2]} show that male names are more likely to be associated with career terms than female names where the association between two terms is measured using embedding similarity, and similarly African-American names are likely to be associated with unpleasant terms than European-American names.
